Computed X-ray tomography (CT) is a 3D viewing technique for the diagnosis of internal diseases. FIG. 1 shows an example of a prior art CT system 100. The system includes an X-ray source 105 and an array of X-ray detectors 110. In CT, the X-Ray source 105 is rotated around a subject 115 by a CT scanner. The X-ray source 105 projects radiation through the subject 115 onto the detectors 110 to collect projection data. A contrast agent may be introduced into the blood of the subject 115 to enhance the acquired images. The subject 115 may be placed on a movable platform 120 that is manipulated by a motor 125 and computing equipment 130. This allows the different images to be taken at different locations. The collected projection data is then transferred to the computing equipment 130. A 3D image is then reconstructed mathematically from the rotational X-ray projection data using tomographic reconstruction. The 3D image can then be viewed on the video display 135.
Magnetic Resonance Imaging (MRI) is a diagnostic 3D viewing technique where the subject is placed in a powerful uniform magnetic field. In order to image different sections of the subject, three orthogonal magnetic gradients are applied in this uniform magnetic field. Radio frequency (RF) pulses are applied to a specific section to cause hydrogen atoms in the section to absorb the RF energy and begin resonating. The location of these sections is determined by the strength of the different gradients and the frequency of the RF pulse. After the RF pulse has been delivered, the hydrogen atoms stop resonating, release the absorbed energy, and become realigned to the uniform magnetic field. The released energy can be detected as an RF pulse. Because the detected RF pulse signal depends on specific properties of tissue in a section, MRI is able to measure and reconstruct a 3D image of the subject. This 3D image or volume consists of volume elements, or voxels.
Image segmentation refers to extracting data pertaining to one or more meaningful structures or regions of interest (i.e., “segmented data”) from imaging data that includes other data that does not pertain to such one or more structures or regions of interest (i.e., “non-segmented data.”) As an illustrative example, a cardiologist may be interested in viewing only 3D image of a certain portion of the aorta. However, the raw image data typically includes the aorta along with the nearby heart and other thoracic tissue, bone structures, etc. Image segmentation can be used to provide enhanced visualization and quantification for better diagnosis. The present inventors have recognized a need in the art for improvements in 3D data segmentation and display, such as to improve speed, accuracy, and/or ease of use for diagnostic or other purposes.